Object representation using colour, shape and structure criteria in a binary partition tree

Binary partition trees (BPTs) are a well known technique used for region-based image representation and analysis. BPTs are usually created as a result of a merging process based on homogeneity properties, such as colour or motion. In this paper, we present a BPT creation technique based on a general merging algorithm, where the homogeneity criteria are neither low-level (pixel oriented, intra-region), nor high-level features (object oriented, semantics), but a combination of several criteria including region-based and structural features such as shape and partial-inclusion. We are thus combining intra-region homogeneity (e.g colour-based) with inter-region homogeneity (structural), with the long term aim of bridging the gap in region-based image analysis from low-level features to a higher level interpretation of the image by the intermediate description of the image structure (which we call "syntactic visual features"). Syntactic visual features are geometric relationships among regions based on shapes and the spatial configuration of neighboring regions in the image and can be found by structure analysis (or syntactic analysis). The aim of this work is to present how the addition of combined pixel-region and structural features leads to better object BPT representation.